1. Field of the Invention
The present invention relates to glucose monitoring systems and, more specifically, to a method to generate alerts, based on a measure of the clinical risk associated to both glucose level and current trend, for hypoglycemia and hyperglycemia prevention in patients wearing such devices.
2. Description of the Related Art
Diabetes is a chronic disease characterized by impaired/absent production of a hormone, insulin, which lowers the glucose concentration in blood after a meal. The standard therapy for diabetes is based on insulin and drugs administration, diet and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day. Given the inefficiency of SMBG approach in capturing the actual extent of glucose dynamics during the daily life, glycaemia (glucose concentration in blood, BG) often exceeds the normality range (70-180 mg/dl). Episodes of hypoglycemia (BG<70 mg/dl) and hyperglycemia (BG>180 mg/dl) are dangerous for the patient mainly in the short term and in the long term for respectively. Short-term consequences are dramatic since they may lead to diabetic coma, while prolonged periods of hyperglycemia are associated to the development of complications such as the diabetic retinopathy and nephropathy.
In the last decade, new devices have become available for the continuous monitoring of the glycaemia. Minimally invasive Continuous Glucose Monitoring (CGM) devices, such as the Dexcom Seven plus, the MiniMed Paradigm Real-Time and Non Invasive Continuous Glucose Monitoring (NI-CGM) are becoming available for clinical practice, providing a measurement of glucose concentration every 1-5 minutes. Such devices are crucial, since they provide a real-time and almost continuous information on patient's glycemic concentration. In addition, most of these devices are provided with an alert generator system, which generates a visual/acoustic alert when hypoglycemic and hyperglycemic thresholds are crossed by the current glycaemia, allowing prompt detection of such threatening events. More important, the almost continuous feature of CGM output allows the use of prediction techniques to anticipate the crossing, and eventually alert the patient of the upcoming event.
Therapeutic actions, such as injection of insulin correction boluses to revert a condition of hyperglycemia- or carbohydrates intake to treat hypoglycemia, cannot avoid exposure of the patients to events that can be threatening, either in the long or short term. In fact, insulin requires about an hour to be effective and to induce appreciable decrease in glucose concentration. Also, carbohydrates take time to reach the blood stream in order to compensate the effects of hypoglycemia. In this framework, generating an alert ahead of the time could give the patient enough time for the therapeutic actions to be effective in avoiding the threats of the event itself.
It is important to provide the patient with information about his/her glycemic status in a smart manner, e.g. by supplying information on the current “clinical risk”. Conceptually, the clinical risk is a measure of the severity of a specific glycemic condition, which depends mainly on the glucose level, but might be influenced also by other factors, such as glucose trend and possibly the quantity of insulin present in the patient's body. In particular, when evaluating the “clinical risk” to which the patient is exposed, one should consider not only the glycemic level but also its trend. Notably, a device which raises alarms on the basis of glucose sensor information and critical risk, should exploit technologies embeddable on a small PDA platform.
CGM Devices and their Alert Systems
Minimally invasive Continuous Glucose Monitoring (CGM) devices currently available in the market, such as the Dexcom Seven Plus (Dexcom Inc., San Diego, Calif.), the MiniMed Paradigm Real-Time (Medtronic Inc., Northridge, Calif.), the Guardian Real-Time (Medtronic Inc., Northridge, Calif.), and the FreeStyle Navigator (Abbott Diabetes Care, Alameda, Calif.), are provided with a visual/acoustic alert generator system that warns the patient when hypoglycemic or hyperglycemic thresholds are crossed. This type of alert is based on the current glycemic value measure by the sensor only. The FreeStyle Navigator or the MiniMed Paradigm Real-Time also embeds another alert generator system for hypoglycemia and hyperglycemia, based on the projection of current glucose level and trend. In particular, the projection method employed in the MiniMed Paradigm Real-Time estimates the current trend using a Savitzky-Golay finite impulse response derivative filter, which is multiplied by a prediction horizon of 5-30 minutes.
Research on Glucose Prediction Algorithms: State of the Art
The real-time prevention of hypo/hyperglycemic events is a natural online application of CGM. As a matter of fact, a few years after the appearance of CGM sensors in the market, some projection methods were proposed to generate alerts when the actual trend of the glucose concentration profile suggested that hypoglycemia was likely to occur within a short time. In Choleau et al., for instance, an hypoalert is generated when the future glycemic concentration, obtained on the basis of first-order linear extrapolation of the last two/three glucose samples, is forecasted to cross the hypoglycemic threshold within 20 min. Similar methods are implemented in commercial devices, with the aim of delivering alerts for dangerous trends.
Also generation of hypo/hyperalerts can be obtained by means of ahead-of-time prediction of glucose concentration calculated from past CGM data. Sparacino et al., demonstrated that simple prediction algorithms based on model with a reduced number of parameters, i.e. either first-order polynomial or first-order auto-regressive (AR(1)) models, with time-varying parameters identified by least squares (LS) using a fixed forgetting factor, are suitable for predicting glycaemia ahead in time with a sufficient accuracy, with a PH of 30 and 45 min. Eren-Oruklu et al. developed prediction algorithms based on AR(3) and ARMA(3,1) models, with time-varying parameters identified by LS, using a forgetting factor μ which could be modulated according to the glucose trend. Reifman et al. proposed a predictor based on an AR(10) model, with time-invariant and subject-invariant parameters identified by regularized LS. Similarly, Gani et al. developed a prediction strategy based on an AR(30) model with time-invariant parameters identified by regularized LS on pre-filtered data. Finan et al. proposed a predictor based on an ARX(3) model with exogenous inputs given by ingested carbohydrates and insulin medications, both with time-invariant and time-variant parameters. Palerm and Bequette, after having posed the problem in a state-space setting, used the Kalman filtering methodology to predict glucose level after a given PH, using a double integrated random walk as prior for glucose dynamics.
Recently, NN models have been the subject of some investigations for glucose prediction. Pérez-Gandìa et al. developed a feed-forward NN for glucose prediction, trained and tested with 3 different PHs, i.e. 15, 30, and 45 min. More recently, Pappada et al. proposed a NN approach to predict glycaemia with a PH of 75 min. Finally, a preliminary study carried out on a limited dataset consisting of only one patient was developed by Eskaf et al. Inputs of their NN model include the first-order differences of the glycemic time series, and information on meals, insulin and physical exercise, extracted directly from the blood glucose time-series, by modeling the glycemic level as a dynamic system.
Margin of Improvements of CGM Devices
Even if several predictive models have been developed to forecast in real time the future glucose level measured by a CGM device, none of the CGM devices currently available in the market is provided with an alert generation system which generates preventive hypoglycemic and hyperglycemic alerts based on the concept of current clinical risk associated to the glycemic value and its trend.
Clinical Risk Measured by the Dynamic Risk Concept
It has been suggested by Kovatchev and colleagues that the study of glucose concentration time series should take into account that the glycemic range is asymmetric, with the “hypo range” much narrower than the “hyper range” with a much faster increase of health threats when moving deeper in the first vs. the latter range. Also, the distribution of glucose concentration values is skewed within the range. In the literature, transformations of the glucose scale into penalty scores have been proposed by Kovatchev, by Hill et al (Glycemic Risk Assessment Diabetes Equation, GRADE), and by Rodbard (Index of Glycemic Control, ICG). These scores are able to equally weight hypo and hyperglycemic episodes. As an example of risk score, we consider Kovatchev's formulation. In this approach, a nonlinear transformation converts every single glucose reading into a “static” risk value, which puts more emphasis on values within the clinically critical regions of hypo and hyperglycemia than in the safe region of normo-glycaemia.
The above mentioned transformations of glucose levels and the correspondent indexes are “static”, i.e. a given glycemic level is associated to a specific penalty or risk score.
Recently a modification of the mathematical definition of risk associated with glucose levels has been proposed by Guerra et al. in order to include in the concept of risk not only the actual glycemic level, but also the glucose trend. Consider for example a glycemic level of 65 mg/dl (mild hypoglycemia) with decreasing or increasing trend. The first case (decreasing trend, negative time derivative) refers to a more threatening condition, since the patient is heading deeper into the hypoglycemic region while in the second case (increasing trend, positive time derivative) the patient is recovering towards the normo-glycaemia. The new risk function, called the Dynamic Risk (DR) includes this information, assigning higher risk to situation in which the trend is leading to a dangerous zone. In particular the risk is increased when glucose concentration is close to or in the hypoglycemic range with decreasing trend and is close to or in the hyperglycemic range with increasing trend. It has been proved that the DR as formulated is intrinsically predictive, since it allows for alert generation about 10 minutes before the actual threshold crossing. Several mathematical formulations/structures of DR can be implemented exploiting variants of those proposed.